ARL is planning for the release of a three commercial transport packages. The most significant contributor to the benefits created, is the extensive use of mathematical algorithms. Here the objectives and intentions of the solutions are shared –together named the e-Fleet Suite:

• ARL Deployment Planner, for visual deployment of transport assets in a network and automated impact calculations of expected performance and costs
• ARL Network Optimizer, for automated trimming of deployments, and later definition of services and full network
• ARL Fleet Manager, monitoring and tracking of performance and costs comparing with initial calculations, including interaction with internal and external business partners

Current Situation

You already have defined your transport network, organized in services with a published transit time and loading capability, provided by a set of close-to-similar transport vehicles (deep or short sea vessels or ferries, trains, barges of truck) deployed for servicing a sequence of repeatable service points, thereby creating a consistent and predictable corridor (point-to-point) loading capability and transit time. Also you have defined vehicle running costs and service point operational costs, and you have set constraints in the network like channel or lock passage slots, high/low tide quay access time slots, border crossing customs points opening hours etc.

Market & Goal Driven Deployment

Imagine if you rather than deploying the transport vehicles yourself, instead describe the market requirements over time for the individual services, and set and prioritize the goals for your network and for individual services. The goal might be:

• Maximum covering of market demands
• Generation of highest yield
• Maximized use of own transport assets
• Lowest cost
• Minimize use of chartered/ leased transport assets

Based on these criteria the rest is math! Deployment of vehicles to meet the set goals best possible, calculating concrete measures for achieving the prioritized goals, as well as calculating any other metrics, like transit times and costs.

Scenarios in Parallel

Imagine a number of scenarios calculated in parallel each meeting different priorities of goals, giving you the ability to compare the characteristics –costs, transit times, capacities, market coverage- and make a deliberate choice of the best deployment plan for you!

Re-Deployment to Reflect Fluctuating Market

Let us go a little further: Imagine your market demands are fluctuating over time, with a variation in the demand for individual products, for example seasonal refrigerated market demands.

Rather than deploying the same set of vehicles over a longer period of time on the same service, let the optimizer deploy assets dynamically using the hubs in your hub-and-spoke network to minimize costs for service re-deployments, meeting the goals optimally not just as an average yearly consideration, but each and every week.

No more Services

And finally: Do you really need to organize you network in ‘services’? Or is this a concept you have applied for the sake of keeping overview yourself? Your customers are interested in specific corridors for a certain period of time. What if you could organize your network to meet your customers’ demands best possible, with no consideration for internal organisation, as all that is done by a software math algorithm, which in parallel can consider many more options, than you can do possibly do manually; which can calculate multiple parameters like costs, transit times, capacities without sweating; which can re-do the calculation dynamically and continuously in order to adjust for operational and commercial realities like delays, weather conditions and ad-hoc market opportunities.

What if your network and deployment would be driven solely by market demands and goals set by you yourself?

This is what math algorithms can do for the transport industry. This is what the ARL guys are doing.